Confeagle: Automated Analysis of Configuration Vulnerabilities in Web Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Web applications and server environments hosting them rely on configuration settings that influence their security, usability, and performance. Misconfiguration results in severe security vulnerabilities. Recent trends show that misconfiguration is among the top critical risks in web applications. While effective at uncovering numerous classes of vulnerabilities, generic web application vulnerability scanners are limited in identifying configuration vulnerabilities. In this paper, we present an approach that effectively combines hierarchical configuration scanning and preliminary source code analysis of web applications to pinpoint potential configuration vulnerabilities, quantify the degree of severity based on standard metrics, and facilitate fixing of vulnerabilities found therein. We implemented our approach in a tool called Confeagle and evaluated it on 14 widely deployed PHP web applications. Unlike generic web vulnerability scanners, on the subject applications, Confeagle detected potential configuration vulnerabilities that could result in information disclosure, denial-of-service, and session hijacking attacks on the applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it